Different methods have been used to classify galaxies in
terms of their morphology. These include visual classification
[1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} and parametric
methods that assume an analytic model for fitting galaxy surface brightness based on its
physical and mathematical parameters [10]}, [11]}, [12]}, [13]}. Non-parametric methods have been also commonly used, by
measuring different galaxy properties that correlate with
morphological types, such as colour, light distribution, galaxy shape,
etc. [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, being
very often mixed with the machine and deep learning for giving galaxy a probability to
be early- or late-type [18]}, [23]}, [20]}, [25]}, [26]}, [27]}, and/or purely based
machine and deep learning methods [28]}, [29]}, [30]}, [31]}, [32]}, [33]}. Each method has
its advantages and disadvantages. Visual and parametric methods are
more used for nearby and well-resolved galaxies, providing a more
detailed information about galaxy structure, while non-parametric and
machine/deep learning methods are less time-consuming and showed to be useful when dealing with large datasets, and higher redshift
sources.
